The purpose of any manufacturing business is to purchase raw materials and/or components and subsequently convert these materials and components into a product of greater value that can be sold for a higher price. It is in this manner that profit is made.
However, in order to be successful, a manufacturing business requires considerable planning. A manufacturer needs to control the types and quantities of materials they are purchasing, plan which products are to be produced as well as determine the quantities needed, and ensure that they are able to meet both current and future customer demand. Improper planning in any of these areas can readily lead to lost sales and decreased profits.
For instance, the purchasing of an insufficient quantity of an item used in manufacturing, or the wrong item, can result in the manufacturer being unable to supply enough of their product to a customer by an agreed upon date. To prevent the above from occurring, many companies will purchase excessive quantities of raw materials or items needed for the manufacturing process. However, this also results in money being wasted, as an excess quantity of materials and items tie up cash while they remain as stock. Similar to stock levels, the timing of a production run is also important. For example, beginning production of an order at the wrong time can lead to a customer deadline being missed, and ultimately, a loss in sales.
To facilitate the planning necessary for a successful manufacturing business, many manufacturers utilize a business planning technique known as Material Requirements Planning (MRP). The typical MRP system is a computer-implemented scheduling procedure for one or more production processes. Generally speaking, MRP systems automate the analysis of certain aspects of a manufacturer's operations in order to provide answers to three specific questions, including what items (i.e., raw materials and finished goods) are required, how many are required, and when are they required by.
FIG. 1 depicts a typical Material Requirements Planning (MRP) system 10, which works on certain input data 12 provided to the system 10 in order to generate some specific output data 14. Data input into the MRP system 10 includes a production schedule 12A, which is a combination of all the known and expected demand over a defined period of time for the products being created. The production schedule provides information on the products being created, how much of the products are required at a time, and when a quantity of products is required to meet demand. Also input into the MRP system 10 is data concerning inventory status 12B, including records of net materials already in stock and available for use, as well as materials on order from suppliers. The MRP system 10 also requires a bill of materials 12C, which provides detailed information on the raw materials, components and subassemblies required to make each product. Lastly, the MRP system must be provided with certain planning data 12D, such as, for example, batch size or maximum amount of a material or item that can be processed at any one time.
The MRP system 10 analyzes the input data and generally provides recommendations on when a batch of product should be produced in order to meet an expected demand, as well as the amount of raw materials or items required for the production of the product. More specifically, the MRP system 10 outputs two types of data. The first output 14A is a recommended production schedule that lays out a schedule of the required minimum start and completion dates for production of a product, along with needed quantities of materials provided in the bill of materials. The second output 14B is a recommended purchasing schedule that lays out the dates that raw materials and components should be ordered as well as received.
Accordingly, the MRP system 10 is an automated set of techniques that analyzes production schedules, bill of materials, and inventory data in order to calculate stock or inventory requirements. The typical system also generates recommendations on when new materials should be purchased so as to maintain an inventory level necessary for the manufacturing of a product.
As such, Material Requirements Planning (MRP) systems are designed to facilitate the day-to-day operation of a manufacturing plant by generating recommended schedules on when production of a product should occur as well as when new inventory of materials and parts should be acquired. These recommended schedules are determined in response to the desired outcome of the manufacturing process as previously indicated to the MRP system (i.e., one desired outcome being the need to manufacture 200 widgets now, and maintain sufficient stock levels so that an additional 200 widgets can be manufactured two days from now). Thus, typical MRP systems focus on the manufacturing schedules necessary to meet a specific production goal, they do not focus on the actual manufacturing process itself, nor do they provide any analysis on how the manufacturing process may be potentially improved.
Similar to MRP systems, Discrete Event Simulators (DES) are a second type of computerized tool frequently utilized in a manufacturing environment. However, unlike MRP systems, Discrete Event Simulators analyze the actual manufacturing process, allowing a user to assess how the efficiency of a particular manufacturing process might be improved.
Specifically, a Discrete Event Simulator (DES) models a manufacturing process and simulates the behavior of the process as time progresses. The DES system evaluates the manufacturing process as consisting of discrete units of traffic that move or flow through a series of steps representing the various stages of an assembly line.
To further illustrate the above point, see FIG. 2, which depicts a process for manufacturing a specific product 24, such as, for example, a widget. One or more initial components or raw materials 20 are first introduced at a first stage 22A of an assembly line. Once initial processing is complete, the raw material 20 is passed through the remaining stages 22B-22F of the assembly line. Certain stages 22A, 22D, 22F may simply act upon or process the existing components of the unfinished widget, while other stages 22B, 22C, 22E supplement the unfinished widget with additional components 23, 25, 27. Ultimately the widget passes through the final stage 22F of the assembly line and becomes a finished product 24 that is ready to be sold.
To accurately model the widget manufacturing process, the DES system can be programmed to emulate the behavior of the various stages 22A-22F of the assembly line. This subsequently provides manufacturing personal with the ability to evaluate how the efficiency of the assembly line is affected in response to either a proposed or actual change to the manufacturing process.
To further illustrate the above point, consider another example wherein a DES system is configured to model the assembly line of FIG. 2. An engineer or other manufacturer personal subsequently alters the virtual behavior of stage 22D of the assembly line, programming the DES system to act as if the components making up stage 22D have been replaced by a newer, more efficient device. The simulated assembly line represented in the DES system is then allowed to run through one pass or iteration of the manufacturing process, thereby allowing the performance of the assembly line as well as any potential problems to hopefully be ascertained.
FIG. 3 illustrates a traditional Discrete Event Simulator (DES) system 30. As depicted in FIG. 3, a traditional DES system 30 typically requires the input of three types of data. The first type of input data includes various operation parameters 32A specific for the manufacturing process/assembly line being evaluated. Parameters include, for example, the number of stations or machines in the assembly line, the product routing, and the available manpower, as well as various operational characteristics such as set-up data, cycle times, etc. The second type of input data includes the duration of the product run 32B. This duration value can be represented, for example, as a number of hours an assembly line is run, or alternatively, the number of units produced. The last type of input data provided to the DES system 30 is the production schedule 32C, which as previously discussed, represents both the known and expected demand for a product over a defined period of time. The production schedule provides information on the products being created, how much of the products are required at a time, and when a quantity of product is required to meet demand.
The DES system 30 subsequently analyzes the three types of input data 32A-32C described above and outputs two pieces of data that generally represents the efficiency of the manufacturing process. The first data output by the DES system 30 comprises one or more values representing a measured utilization or efficiency 34A of the machines and associated workers that make up the assembly line. From this data the manufacturer can determine, for example, the number of man hours that would be consumed by the simulated manufacturing process if it was actually implemented in real life. The data also provides a measurement of the percentage of time that a worker and their associated workstation were active verses idle. The second piece of data output by the DES system 30 comprises the estimated number of products that would be produced if the simulated manufacturing process were implemented in real life.
Accordingly, Discrete Event Simulators (DES) provide manufacturing personal with the ability to simulate a manufacturing process, and then determine how certain changes to one or more steps of the process affect the manufacturing efficiency for a product as indicated by resource utilization and number of products produced. Although useful, traditional DES systems are typically restricted in their functionality, being limited to providing information concerning manufacturing capacity, and process effectiveness comparisons for a single iteration of a manufacturing cycle, i.e., shift, day, week, month, number of hours, etc. Consequently, DES systems are typically considered useful primarily just for evaluating alternative approaches to process improvement.
Similar to other existing computer-based manufacturing aids, DES systems provide no insight or assistance on how proposed or actual changes in a manufacturing process effect the financial statements of the manufacturing business. Similarly, DES system are typically configured to only operate for a single manufacturing cycle, whereby the assembly line under investigation is activated for only a single run once the necessary input data is received by the DES system. Consequently, even if DES systems were capable of providing information concerning how changes in the manufacturing process impact the financial statements of the business, the resultant information would still be of questionable relevance due the DES system's lack of conducting repeated test cycles that allow for generated data to be fed back into the process and further refined.